Abstract
Rank join can be generalized to sets of relations whose objects are equipped with a score and a real-valued feature vector. Such vectors can be used to compare the objects to one another so as to join them based on a notion of “proximity”. The problem becomes then that of retrieving combinations of objects that have high scores, whose feature vectors are close to one another and possibly to a given feature vector (the query). Traditional rank join algorithms may read more input than needed when solving proximity rank join. Such weakness can be overcome by designing new algorithms for which, as in classical rank join, bounding scheme (and a tight version thereof) and pulling strategy play a crucial role to efficiently compute the solution.
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Martinenghi, D., Tagliasacchi, M. (2011). Proximity Rank Join in Search Computing. In: Ceri, S., Brambilla, M. (eds) Search Computing. Lecture Notes in Computer Science, vol 6585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19668-3_11
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DOI: https://doi.org/10.1007/978-3-642-19668-3_11
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